Browse Skills

Draft a professional customer-facing response tailored to the situation and relationship. Use when answering product questions, handling escalations or outages, delivering bad news, declining feature requests, or replying to billing issues.

Profile and explore a dataset to understand its structure, quality, and patterns. Helps when encountering new data—checks null rates, column distributions, and identifies issues like duplicates or outliers.

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types.

Drafts publish-ready knowledge base articles from resolved support issues, common questions, or documented workarounds. Structures content for searchability and self-service.

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use for analyzing RNA-seq, WGS/WES, or ATAC-seq data from local FASTQs or public GEO/SRA datasets.

Helps scientists systematically select research problems, generate ideas, troubleshoot projects, and make strategic decisions through a structured conversational framework.

Provides deep learning-based single-cell analysis workflows using scvi-tools for data integration, batch correction, multi-modal analysis, spatial transcriptomics, and RNA velocity.

Performs quality control on single-cell RNA-seq data using scverse best practices with MAD-based filtering and comprehensive visualizations. Use for QC analysis, filtering low-quality cells, or assessing data quality in .h5ad or .h5 files.

Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries.

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

Triage and prioritize a support ticket or customer issue. Use when a new ticket comes in and needs categorization, assigning P1-P4 priority, deciding which team should handle it, or checking whether it's a duplicate or known issue before routing.

Review analyses for methodology, accuracy, and bias before stakeholder presentations. Spot-check calculations, verify SQL results, and assess if conclusions are data-supported.